Max Pooling
Max Pooling is a sample-based discretization process in CNNs. It divides the input image into sub-regions and outputs the maximum value from each sub-region, reducing dimensional size.
Frequently Asked Questions
Why use max pooling in CNNs?▼
To reduce spatial dimensions (parameter counts) while retaining dominant visual features, helping build translation invariance.
What is average pooling?▼
A variant that outputs the mathematical average of a sub-region instead of the maximum value.
Quick Facts
- CategoryNeural Architectures
- Key ApplicationCNN downsampling, feature size compression, and computer vision feature extraction.
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